Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-17T15:20:56.419Z Has data issue: false hasContentIssue false
  • English
  • Français

Acquiring expert knowledge from characterized designs

Published online by Cambridge University Press:  27 February 2009

Sally McLaughlin  and
John S. Gero
Sally McLaughlin
Affiliation:
Architectural Computing Unit, Department of Architectural Science, University of Sydney, NSW 2006, Australia
John S. Gero
Affiliation:
Architectural Computing Unit, Department of Architectural Science, University of Sydney, NSW 2006, Australia
Get access Rights & Permissions [Opens in a new window]

Abstract

The expertise of designers consists, primarily, of information about the relationship between goals or performance criteria and the attributes of the desired artifact that will result in performances that will satisfy these criteria. The designer like experts in other fields is typically better at applying the knowledge that constitutes his expertise than he is at articulating this knowledge. Generation and simulation models are discussed as a means of generating a set of designs for which the set of attributes defining these designs and the performance of these designs in terms of the criteria considered are explicitly defined. Pareto optimization is discussed as a means of structuring these designs on the basis of their performance. The induction algorithm ID3 is used as a means of inferring general statements about the nature of solutions which exhibit Pareto optimal performance in terms of a set of performance criteria. The rules inferred in building design domain are compared with those extracted using a heuristic based learning system.

Type
Research Article
Information
AI EDAM , Volume 1 , Issue 2 , May 1987 , pp. 73 - 87
Copyright
Copyright © Cambridge University Press 1987

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bundy, A., Silver, B. and Plumer, D. 1985. An analytical comparison of some rule-learning programs, Artificial Intelligence 27 (2), 137–181.CrossRefGoogle Scholar
Coyne, R. D., Rosenman, M. A., Radford, A. D. and Gero, J. S. 1987 Innovation and creativity in knowledge-based CAD. In Gero, J. S(ed.), Expert Systems in Computer-Aided Design, Amsterdam: North-Holland, pp. 435–465.Google Scholar
Coyne, R. D. and Gero, J. S. 1986. Semantics and the organisation of knowledge in design, Design Computing 1 (1), 68–69.Google Scholar
Gero, J. S. and Radford, A. D. 1984. The place of multicriteria optimisation in design. In Landgon, R. and Purcell, P. (eds), Design Theory and Practice, London: The Design Council, pp. 81–85.Google Scholar
Mackenzie, C. A. and Gero, J. S. 1987. Learning design rules from decisions and performances, Artificial Intelligence in Engineering 2 (1), 2–10.CrossRefGoogle Scholar
Michalski, R. S. 1986. Understanding the nature of learning: issues and directions. In Michalski, R. S., Carbonell, J. G. and Mitchell, T M. (eds). Machine Learning. An Artificial Intelligence Approach, 2, Los Altos: Kaufmann, pp. 3–25.Google Scholar
Pareto, V. 1971. Manual of Political Economy, New York: Kelly.Google Scholar
Quinlan, J. R. 1983. Learning efficient classification procedures and their application to chess endgames. In Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (eds), Machine Learning: An Artificial Intelligence Approach, Palo Alto: Tioga, pp. 463–482.Google Scholar
Radford, A. D. and Gero, J. S. 1980. Tradeoff diagrams for the integrated design of the physical environment in buildings, Building and Environment 15, 3–15.CrossRefGoogle Scholar